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Category: Analytics

How AI Drives Innovation in Next Generation Cloud Business Intelligence?

Posted on by Imarticus Learning

Today, we have access to a huge amount of technology and other systems through the internet – Artificial Intelligent systems are one of those. AI is becoming a larger part of our lives with each passing day, and the chances are that AI systems would already have affected us in some way or the other.
AI, in essence, is a predictive technology. The main function of every AI system is to essentially make a prediction based on the amount of data and information that it analyses. Since it can sift through any large amount of data, it is thus a type of technology that improves our lives in a huge manner. Similarly, the role of business intelligence and business analytics has changed too – it is now something that deals with increasing amounts of predictive analysis rather than historical analysis, and is available to users as an interactive, easy-to-use tool.

Thought Spot

Thought Spot is one of the pioneers in the segment of Business Intelligence – the California based company can be credited for creating a Google-like search engine which can analyse large amounts of data quickly and completely so as to provide the user with some great insights into the data. Thought Spot’s Ad-hoc version of data analytics provides various amazing services, like extremely transparent calculations into how each insight was derived, accompanying of natural language narratives with the rendered charts and a guided, curated search experience which generates suggestions for the users based on the role, the data model and the search history of the person. Thought Spot and its data analytics model is truly something to watch out for, in the future.

Anticipatory Models

Companies like Thought Spot and other data-driven Business Intelligence organisations are considered to be the forerunners of the next, and perhaps the largest wave in Business Intelligence called the anticipatory intelligence. They aim to leverage the usage of AI in a number of scenarios, like anticipatory devices, conversations and contexts. In this first one, the aim is to automate something that a large number of users are trying to do in a small time period so that it happens quicker and better. In the second and third, natural language processing systems are used so as to predict what the users are going to say, and thus promote rapid communication.
If all of this fascinates you, you should definitely look at the business analytics training courses and the data science courses that Imarticus Learning has to offer.

Household Electricity Consumption – Machine Learning Algorithm

Posted on by Imarticus Learning

Power supply, generation, and its billing generate a huge amount of data. ML actually makes it possible to learn from this data and use an algorithm to accurately predict future occurrences like volumes of load and its demand, snag identification, efficiency and power loss reduction, problems and logistics involved in metering and billing and everything in between from power generation to its billing and beyond.
Machine learning courses in India could teach you how to understand ML and data analytics, so you aid ML to perform at its best in predicting outcomes. The Algorithm in ML for household electricity consumption works on data drawn from smart meters, solar panels, and data regarding the usage of electricity at different times of the day.
This huge data comprises the multi-variable time-series, and the algorithm can successfully predict future consumption. In real terms, the ML algorithm can predict such information as to help make the power generation and supply system more efficient.
Obviously, there are many steps involved in helping the machine take data in its raw multivariate form and enabling it to arrive at the future consumption prediction. This is where Machine learning courses come in handy. You can learn the techniques of ML involving predictive strategies like the direct methods and the recursive ones.
A good idea is to also incorporate learning of Big Data Hadoop training courses that can help one understand strategies, working of ML and data analytics. The logic of the process of algorithm development would be developing

  • The framework development for evaluation of non- linear, linear, and ML ensemble algorithms.
  • Evaluation of ML as it uses the strategy of forecasting the time-series both by the direct daily method and the recursive method.

Again such processes involve

  1. Describing the problem.
  2. Preparing and loading the data set.
  3. Evaluating the model.
  4. Recursive forecasting.
  5. Multi-Step direct forecasting.

Through highly accurate predictions ML helps the algorithm to plan future power generation, reduce transmission losses, tweak the metering, billing and collection systems and so much more. Once you master such algorithms, ML and data analytics, the scope of applying ML to various and everyday issues on a real-time basis, open the wide world of opportunity and good remuneration to you.
Yes, ML and data analytics use Python framework which has immense scope for progress basically because it can predict the outcomes of simple and complex tasks, single and multi-variate tasks, and even makes single and complex predictions by learning from the data, filling in the missing values, creating new values and so on. And to learn an ML course is essential. Start today and soon you will be able to master such tasks quite easily.

Reference:
https://machinelearningmastery.com/multi-step-time-series-forecasting-with-machine-learning-models-for-household-electricity-consumption/

Developing ML Models in Multivariate, Multi-Step Forecasting of Air Pollution Time-Series

Posted on by Imarticus Learning

Machine Learning Courses in India

The ML algorithms can be applied forecast weather and air pollution for the subsequent 3-days. This is challenging because of the need to accurately predict across multivariate input with noisy dependencies that are complex and multi-step, multi-time input data while forecasting and performing the same prediction across many sites.
‘Air Quality Prediction’ or the Global Hackathon EMC dataset provides weather conditions across various sites, and needs accurate predictions of measurement of air quality to provide a 3-day weather forecast.

The Need for Machine Learning

The primary benefits of Machine learning courses are that with them, you can learn to operate the tools from a Python open source library and gain expertise in

  • Providing for missing values, transforming the time-series data and successfully creates models that are worked by the trained and supervised-learning algorithms.
  • Evaluate and develop both linear and nonlinear algorithms to handle the multivariate, multi-step, multi-time series forecast.

The Need for Data Analytics

A real-time problem when working with this dataset is that of missing values and multiple variables drawn from many physical sites. This means integrating and helping the ML algorithm predict and forecast accurately. You will need data analytical skills to achieve this.
The Big Data Hadoop training courses can provide you with skills and learning in

  • Imputing values that are missing, helping algorithms with supervised learning by transforming the input data time-series and creating a requisite number of models using the data and the algorithm.
  • How to evaluate and develop suites of nonlinear and linear algorithms for multiple-step forecasting of a time series.

The Entire Process

Developing this algorithm and making it successfully predict with accuracy the weather forecast over the next 72 hours in an environment that has multiple variables, multiple data sets, some missing data, and lots of ways to develop the code on the Python platform has nine parts.
Namely,

  • Description of the problem.
  • Evaluation of models.
  • ML Model creation.
  • Data preparation using ML.
  • Creating a Test Harness for model evaluation.
  • Linear Algorithms evaluation.
  • Nonlinear Algorithms evaluation.
  • Lag Size tuning.

Benefits of ML, in this case, are handling features that are irrelevant, the ability to support between-variable noise and noisy features, and the ability to support inter-variable relationships. ML forecasting provides both recursive and direct forecasts.
Benefits of data analytics relevant here are in preparing data, feature engineering, lag-tuning the meteorological variables, creating models across many sites, and tuning the algorithm itself.
Enrol in the most suitable course that will help you learn how to develop an algorithm for air pollution forecasting.

Reference:
https://machinelearningmastery.com/how-to-develop-machine-learning-models-for-multivariate-multi-step-air-pollution-time-series-forecasting/

How Data Sciences Principles Play an Important Role in Search Engines

Posted on by Imarticus Learning

Organisations today have started using data at an unprecedented rate for any and everything. Hence, it is mandatory that any organisation that has adopted data will need to analyse the data. Here is the real job of a search engine which can search and get results back in milliseconds.
The notion where people believe search engine is only used for text search is completely wrong as search engines can find structured content in an enhanced way than relational databases. Users can also check on portions of fields, such as names, addresses at a much quicker pace and enhanced manner. Another advantage of search engines is that they are scalable and can handle tons of data in the most easier and faster manner.
Few of the benefits of using search engine tools for data science tasks which are taught in big data analytics courses include:
Exploring Data in Minutes: Datasets need to be loaded to search engines, and the first cut of analysis are ready within minutes sans codes. This is the blessing of modern search engines that can deal with all content types including XML, PDF, Office Docs to name a few. Although data can be dense or scarce, the ingestion is faster and flexible. Once loaded the search engines through their flexible query language can support querying and the ability to present larger result sets.
Data splits are Easier to Produce: Some firms use search engines as a more flexible way to store data sets to be ingested by deep learning systems. This is because most drivers have built-in support for complex joins across multiple datasets as well as a natural selection of particular rows and columns.
Reduction of Data: Modern search engines come with an array of tools for mapping a plethora of content which includes text, numeric, spatial, categorical, custom into a vector space and consist of a large set of tools for constructing weights, capturing metadata, handling null, imputing values and individually shaping data according to the users will.
However, there is always room to grow there is an instance where modern search engines are not ready for data science and still evolving. These areas include analysing graphs, iterative computation tasks, few deep learning systems and lagging behind search support for images and audio files. There is still room for improvement and data scientists are working towards closing in on this gap.

How Machine Learning Helps in Psychiatric Epidemiology

Posted on by Imarticus Learning

In India, where, as per medical surveys, every sixth child needs medical supervision for health conditions, schizophrenia is often left untreated and diagnosed. It can cause lifelong trauma and is a severely disabling illness with hallucinations, cognitive impairments, and delusions. Early diagnosis and the use of anti psychotic drugs are imperative. Predicting the course of the illness and treating it with a suitable drug is often by trial-and-error manual offline learning. That’s where the use of ML and AI in the epidemiology use in psychiatric illnesses holds immense potential and scope for growth.
Especially in our country with expensive treatment, lack of medical facilities dedicated to such mental illnesses and a huge population being rural poor being real deterrents.

ML In Predictive Analysis of Responses and Treatment

Improved MRI tools enable visualization of the smallest brain structures like sub fields in the hippo campus. The study is crucial in the treatment of psychiatric sicknesses like schizophrenia where the early recognition and assessment of the thickening or volumetric changes in these fields detected by neuroimaging can be used in morphometry and predicting cognitive declines in the pathology of the hippo campus.
AI is used in the diagnosis of schizophrenia reporting recent onset and not using treatment also known as (first-episode drug-naïve) FEDN. ML and suitable architectural frameworks help researchers evaluate and interpret these MRI scans and brain signals of the hippo campus.
The correlations of information between other cortical regions and the signals of the superior-temporal cortex got from resting-state MRIs are used by the algorithm to identify schizophrenia patients and the response to specific antipsychotic treatments very accurately.
You can now learn all about such ML, big data analytics and AI developments and uses through machine learning courses.

3d-Cnn Spatial Image-Classification

3D-CNN are convoluted neural networks used for 3D modelling, and LiDAR (light detection ranging) data classification under supervision. Cranial Imaging, occurrences of neural events and surveillance are now computer aided and should necessarily be part of Big data Hadoop training courses.

Machine Learning and Predictive Analytics

The best example is of the Alberta University study using an ML algorithm, and MRI visualized images of treated, diagnosed, untreated and healthy persons. Hippo-campus sub field volumes were used to predict responses using regression of support-vectors. The SVR-input was normalised to normal feed levels and split randomly in the module for cross-validation and datasets training in sci-kit. The prediction model and its features were accurately calculated using an inbuilt datasets training-LOOCV.
Machine learning courses in Indiainspired by the technological advancements and uses in psychiatric epidemiology are quickly adopting new content in an innovative move to use ML for predictive analysis.

Build Your Own AI Applications in a Neural Network

Posted on by Imarticus Learning

Today Big Data, Deep Learning, and Data Analytics are widely applied to build neural networks in almost all data-intensive industries. Machine learning courses in India offers such learning as short-term courses, MOOCs, online classrooms, regular classrooms, and even one-on-one courses. Choices are aplenty with materials, tutorials and options for training being readily available thanks to high-speed data and visualization made possible by the internet.
The study on jobs in Data Sciences says that core skills in Python are preferred by recruiters and is requisite for jobs in data analytics. The challenge lies in formulating a plan to study Python and the need of a specialist to help understand the technical and practical aspects of this cutting edge technology.

Why do a Specialization Course for Beginners?

Not all are blessed with being able to learn, update knowledge and be practically adept with the Python platform. It requires a comprehensive knowledge of machine learning, understanding of data handling, visualization techniques, AI deep learning, statistical modelling and being able to use your expertise on real-time practical examples of data sets from various industries.
Machine learning courses and case studies on Python platform are conducted in flexible learn-at-your-own-pace sessions in modes like instructor-led classroom sessions at select locations, virtual online classes led by certified trainers or even video sessions with mentoring at pre-determined convenient times.
One can do separate modules or certificate Big data Hadoop training courses with Python to understand data science analytics and then opt for modules using AI for deep learning with Python or opt for a dual specialization by doing the beginners course and courses covering AI and Deep Learning with Python. The areas of Deep Learning and AI both require prior knowledge of Deep Learning, Machine Learning, and data analytics with Python.
An example of one such course is the AnalytixLabs starter classes in Gurugram and Bangalore as a speedy boot-camp followed by a package of two courses in AI Deep Learning with Python and the Data Science with Python. The prerequisites are knowledge of at least one OOPs language and familiarity with Python. Their 36 classes, 250-hour course offers dual specialisations, and 110 hours of live training using multiple libraries in Python.
Just ensure you choose the right course to allow your career prospects to advance and allows further learning in Python-associated specialised subjects.

How AIML Can Facilitate a Holistic Digital Transformation of SMEs

Posted on by Imarticus Learning

Using AI digitised mobility-efficient business management empowers SMEs to expand to any region globally with literally no associated monetary or infrastructural deterrents. Especially in processes like strategy-based planned sales, financial management, supply chain logistics, and marketing management where the focus should rightly be on the operational aspects rather than offline management of these which reduce enterprise efficiency.
Notable benefits of machine learning courses in India are learning better workflow management, enabling operational management to reach out, service and retain the all-important customer base. Increased cost-reduction, increased satisfaction levels of customers, doing away with time-consuming redundant offline process management and the obvious maximising of profit margins and enterprise efficiency result.

Role of Machine Learning-ML and AI

Issues are unique to every enterprise. Solutions should emerge from the workflow and be need-specific to the enterprise and its segment. Automating the logistics of the supply chain processes and sales can be optimised by ML and AI to build solutions meeting the needs and precise requirements of any business or industry with a high level of precision and customisation through the proper use of the huge data repository available with them.

Data and Challenges

Data is the backbone of automation and readily available with SME’s. Greater volumes in the database ensure tweaking for quickening and process efficiency. Big data Hadoop training courses help streamlining data, identifying and eliminating unnecessary recurrent processes and automating the process for fixed quicker and efficient outcomes is what ML, data analytics and AI intuitive combinations does when customizing processes and big data.
This indirectly frees-up the crucial time-component spent on customer interactions. ML and AI bring huge benefits in pattern recognition and predictive analysis. Their use helps deliver effective business solutions with quick outcomes by identifying and automating recurring procedures and patterns. Thus the digitization of marketing and sales drive profit and efficiency in the enterprise.

Customer Service Paradigms

In today’s scenario the pervasive use of the internet, use of digital tools, mobile apps and smart-phones create a huge database of young consumers under-35, who use and prefer digital methods to offline methods. Gainful insights are provided through their feedback, need for value-enhanced solutions, customer interaction and resolutions for customer satisfaction.
The success of SME’s depends on adapting and catering to this sector which forms nearly two-thirds of the total Indian population. Many shy away from building a digital infrastructure citing prohibitive costs involved. But, as per digital customers and a study by Google-KPMG, SMBs and SMEs have the potential to grow twice as fast with the adaptation of ML and AI.
Do we need to say anything more for machine learning courses?

Facts on Machine Learning and Statistics

Posted on by Imarticus Learning

All machine learning courses in India need proficiency in statistics. However ML is not only statistics but definitely draws inspiration from analysis of statistics. This is so because data is their common factor. An ML-engineer though must and should have proficiency in statistics, while an ML-expert needs to only have sufficient knowledge of basic statistical techniques and data management. Let’s look into why this is so.

Overlaps of Machine Learning and Statistics

Machine learning courses of today borrow concepts like data analysis and statistical modelling to arrive at predictive models for ML. Machine Learning is a branch of computer science while statistics deals with the analysis of statistics in pure mathematics. However, they are interdependent mathematical applications both dealing with the analysis of data, data models, and problem-solving.
It goes without saying that statistics is the older sibling and yet today even statisticians use ML to achieve its end results with Big Data and for Predictive Analysis. Similarly, ML draws on statistical analysis though its aim is entirely different. That’s why Big Data Hadoop training courses also need knowledge of statistics and database management.
Mostly the overlap and confusion occur because both use algorithms and data to predict the end results. However, it is incorrect to equate the two, which are separate advanced fields, in two different branches. They are at best complementary interdependent fields which can aid each other much like siblings often do. Two separate individuals, completely different, in one environment, and with individual destinations. Sure they walk the same path at times!

Clearing the Confusion

Statistics uses a model with defined parameters fitting the data tested through classification and regression techniques to account for clustering and density estimation, to provide the best inference. ML works with networks, graphs and bar charts learning from general data through assigned weights using unsupervised learning techniques to give an accurate prediction of outcomes.
Looking very closely into the two one will notice that ML has no set rules, equations, parameters, variables or assumptions. It learns from the data input and provides a predictive outcome. In statistics, you get an inference unique to a small data set with fixed variables and based on strict regression and classification techniques of mathematical equations. Though older, statistics is pure math. ML is a carefree youngster, which uses and learns from past data, has no limit to data used or variables present and works with algorithms that govern data to give an accurate predictive outcome.
An ML Engineer and Statistician may have areas where their jobs overlap. They share a common path through the use of modelling and data and then branch out to their own destinations. Truly they are complementary in nature bring out the best in the other and helping each other achieve individual end results.

Artificial Intelligence Provides Operational Solutions for the Food Industry!

Posted on by Imarticus Learning

Though Artificial Intelligence (AI) technologies have supported industries in multiple ways, the key is to identify areas specific to each industry where AI solutions are the most relevant. In the case of the food industry, solving operational efficiencies seems to be the area where AI-based solutions can make the maximum impact. And no wonder, with the relatively short timelines that food can be stored before consumption, make this an understandable challenge.

AI or machine learning relies a lot on historic data and uses this information to make predictive solutions or suggestions that can help in foreseeing certain outcomes. The more data at hand, the more closer to accuracy the solution/suggestion is.

Considering this, here are the potential avenues for the use of AI in the food business in ways that could transform conventional modes of operation by increasing efficiencies and production, predicting, assessing, and accurately solving more market demands and more.

Forecasting
Companies have used AI to determine and analyze demand variations, shopping trends during marketing campaigns, and sales drops. Stored data for these variables help machines identify problem areas and solve for them specifically.

It answers questions like – what is the optimal shelf space for this product to ensure increased sales? Which categories perform best for a specific type of promotion? How much should a certain product be stocked during peak/low sales periods? This helps optimize processes and reduce wastes through AI’s intelligent data-back prediction systems.

Boosting Productivity
Cloud computing technology, Big Data analytics, and data-driven machine learning has equipped a lot of industries to streamline their operational efficiencies. In the case of food industries, the manufacturing arm, in particular, AI assists in aiding the production processed by making certain decisions easier through its predictive features. These real-time solutions can potentially save a lot in time and moolah. These cost benefits will in turn be reflected in market satisfaction through pricing.

Automation
Technology’s increased agility in handling fragile produce helps in automating manufacturing tasks in the food industry’s operational chain. This means, tools have become advanced enough to handle delicate food items and process them without damaging them, such as eggs or tomatoes.

Not only this, but automation helps in reducing manual effort in repetitive tasks, therefore, adding time efficiency. This is especially useful for tasks where is lower decision-making potential.

Consumer Preferences
Through AI’s capability to handle large amounts of data with multiple variables and therefore make accurate predictions, consumer preferences can be assessed through their older buying / consuming patterns. Not only does this help in the development of newer products and services but also capitalizes on the key sale-drivers with eagle-eyed consistent focus.

Applications
Some ways to apply AI or machine learning in these industries would be through smartphone apps that fit into the consumer’s lifestyle, such as fitness apps, food suggestion apps based on certain body types, etc. Chatbots for online food partners could be another potential application. Quick food manufacturing machines independent of human assistance is another use case.

Conclusion
What this means for the food industry is that there is a constant need to keep an eye on AI trends and the way it is affecting businesses. Choosing the right AI tool for a certain business is a sure-shot way of intelligently increasing efficiencies and reducing costs. There are many more upcoming AI solutions in the market – keep your eyes on the radar to assess the best solution for your business!

Big Data for Big Banks – You Should Know

Posted on by Imarticus Learning

The growth of Big Data
Data is not just the new oil, but the new land too. In short, data is perhaps the most important resource to have in this century. With billions of data points and information being collected across the world every second through the internet and other avenues, the data size is increasing manifold. The upcoming technology is focusing on how to organize and sort this huge amount of data to derive insights and read patterns.
This, in effect, is referred to as Big Data Analytics Courses. Every major or minor firm, big or small player, in the consumer retail sector to healthcare and financial series, is using insights generated out of this big data to shape and grow their businesses. The lending business is no exception and can benefit immensely from the use of data. Fin-tech is changing the way the banking industry operates and making banking operations smoother, automated and more cost-effective. From fraud mitigation to payment solutions, Fintech is changing the way we think about banks.     
Data in lending business  
From the origination of the role to its continuation and life cycle management data can drive decision making in lending business. The patterns that can be read out of consumer data can predict the loans requirement, the capability of repayment of loans, the frequency of late payments or defaults and even the need for the consumers to refinance their loans. The fin-tech start-ups have already begun using the data in such a way, and hence the alternative lending businesses have bloomed over the last few years. Many banks are either merging with such alternative business lenders or taking the help of third party service providers to help boost their capabilities and skills to use big data analytics in business.
The areas of thrust
The major areas where lending business can be aided through the use of big data analytics are the portfolio risk assessment, stress tests, default probabilities and predicting the loan patterns of consumers. Credit card business already uses such technology extensively in assessing and evaluating their consumers.
For example, the credit card issuers tracked the repayments data of the users and based on the profession or the region; they may at times predict if the balances are going to be resolved or if they are going to be paid up front. They then design their marketing strategies keeping the results of analytics in mind in those areas or regions or regarding those specific consumers.
In the bygone years, the only way banks used to evaluate the creditability of a prospective borrower was to assess his or her records of past loans and repayment history. However, with new real-time data points, banks can study behavioural patterns and take appropriate decisions. Refinancing loans is another important area where technology and finance have come together to make life easier for consumers and banks alike. 
The algorithms can predict when a borrower may need to refinance his loans and can credit the amount in his account within seconds without all the paperwork and unnecessary delays. Another area that has transformed with the advent of big data and technology is the internal auditing of banks. With a digital record of every transaction or decision-making process, compliance rules and regulations are now easier to adhere to and track. 
Lastly, and perhaps most importantly, customer feedbacks have become important in this industry like never before. The algorithms can sift through loads and loads of data in the form of feedbacks and can implement solutions to enhance customer experiences on a real-time basis. Technology has changed almost everything around us and the lending operations to are no exception to the rule. In the years to come, banking may undergo a drastic transformation with elements that at this time, we may even be unable to imagine.
 

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